lmboot: Bootstrap in Linear Models
Various efficient and robust bootstrap methods are implemented for
linear models with least squares estimation. Functions within this package
allow users to create bootstrap sampling distributions for model parameters,
test hypotheses about parameters, and visualize the bootstrap sampling or null
distributions. Methods implemented for linear models include the wild bootstrap by
Wu (1986) <doi:10.1214/aos/1176350142>, the residual and paired bootstraps by
Efron (1979, ISBN:978-1-4612-4380-9), the delete-1 jackknife by
Quenouille (1956) <doi:10.2307/2332914>, and the Bayesian bootstrap by
Rubin (1981) <doi:10.1214/aos/1176345338>.
| Version: |
0.0.1 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
evd (≥ 2.3.0), stats (≥ 3.6.0) |
| Published: |
2019-06-03 |
| Author: |
Megan Heyman [aut, cre] |
| Maintainer: |
Megan Heyman <heyman at rose-hulman.edu> |
| License: |
GPL-2 |
| NeedsCompilation: |
no |
| CRAN checks: |
lmboot results |
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